Every wave of new technology produces the same pattern. A genuine breakthrough creates a feeding frenzy of companies racing to attach themselves to it.
Most never deliver anything that meaningfully changes how their industry operates. A handful do. The difficulty, especially for buyers, investors, and operators, is telling the difference in real time.
Damian Creamer, founder and CEO of StrongMind, has a framework for making that distinction. He developed it inside the education technology category, watching “AI-powered” learning products hit the market over the last two years.
But the framework itself has nothing to do with edtech. It applies anywhere there is a hot technology category and a flood of companies claiming to innovate inside it. Within Creamer’s mindset is a simple but fundamental idea: Innovation is only valuable when it is scalable.
It is a deceptively simple frame, and one of the cleanest tests available for separating real AI innovation from expensive window dressing.
Two Common Failure Modes
Creamer’s framework names two distinct ways companies fail to actually innovate, and confusing them is part of the problem.
The first failure mode is the science project. This is the company with a genuinely interesting demo or impressive proof of concept that captures attention because the underlying technology is novel.
The trouble starts when that novelty has to be productionized. The thing that worked beautifully in a controlled environment turns out to be unworkable in the real one. Science projects can win awards. They cannot win markets.
The second failure mode is execution. This is the company that takes proven, well-understood technology and ships it competently. The product works. It scales. It generates revenue. But there is nothing genuinely new about it.
It is an incremental improvement on what already exists, dressed up in marketing language. Execution-only companies are often profitable. They are not innovating, no matter how the press release reads.
The trap most companies fall into is mistaking one of these for innovation. A startup with a clever demo thinks it is innovating because the technology is novel, but the architecture cannot scale beyond the demo.
An incumbent with a large installed base thinks it is innovating because it has shipped a chatbot integration, but the product underneath is unchanged. Neither company is actually moving the field forward, however confident the marketing.
Why the AI Wave Has Made This Worse
What Damian Creamer‘s framework illuminates is how unusually distorting the current AI moment has become. Foundation models have made it trivial to produce demos that look like genuine breakthroughs. A weekend of work can yield a chatbot, a summarization tool, or a “personalized” assistant that looks impressive in a controlled walkthrough.
The science project category has, accordingly, ballooned. Companies with genuinely novel ideas but no path to scale them are everywhere right now. They are getting funded, written about, and sometimes acquired, on the strength of demos that will not survive contact with production scale or the complexity of serving actual customers over time.
At the same time, the execution-only category has also exploded. Established companies that have not changed their underlying products are bolting AI features onto existing platforms and rebranding themselves as AI-native. The chatbot is real. The wrapper is real. What is novel about the integration, beyond the fact that it exists, is often very little. The platform underneath does the same thing it always did. The AI layer is a coat of paint.
Both categories can effectively attract attention and possibly even investments. Neither, according to Creamer, count as innovation.
What Real Innovation Actually Requires
The harder work, and what Creamer is building toward at StrongMind, is the integration of both. A genuinely novel approach engineered to scale. A capability that did not previously exist, delivered in a form that can serve large populations of users with reliability, economics, and durability that hold up over time.
In Creamer’s view, real AI innovation is not about adding features on top of existing platforms. It is about building the underlying layer correctly from the beginning.
He talks about this in terms of infrastructure: a foundational intelligence layer that sits beneath the product, makes new capabilities possible at scale, and is engineered as core architecture rather than retrofitted as a feature set.
The framing is testable. When you look at a company claiming to innovate with AI, you can ask straightforward questions.
Is the AI capability load-bearing infrastructure, or pinned to the side of an unchanged product? If the AI were removed, would the product still work the same way?
These questions tend to separate the real thing from the marketing exercise quickly.
How to Use the Framework as a Buyer or Investor
For anyone evaluating AI claims right now, Damian Creamer’s framework collapses a complicated assessment into two questions.
Question one: Is this novel? Is the company doing something the field could not do before, or is it repackaging existing capabilities in a way that mostly affects how they are described?
If the underlying capability is two years old and broadly available, the company is competing on execution, rather than innovation. That can still be a good business, but it is not a leap forward.
Question two: Does it scale? Does the demo hold up at 10,000 users? At 100,000? Does the cost structure work? If the product breaks down outside controlled environments, the novelty is academic.
A company that fails the first test is doing execution. A company that fails the second is running a science project. A company that passes both, and there are not many right now, is innovating.
The Discipline This Requires
Creamer’s framework is, in its quieter moments, a discipline more than a definition. It forces founders, buyers, and investors to slow down and ask whether the thing in front of them is actually what it claims to be.
It refuses to be impressed by the demo alone or the scale numbers alone. It insists that real innovation has to satisfy both conditions simultaneously.
In a moment when “AI-powered” has become the most overused phrase in technology marketing, that discipline is unusually valuable. Damian Creamer’s framework will not catch every fake claim. But it will catch most of them.
“Innovation demands both,” he says. It is the cleanest sentence anyone is offering on what real AI innovation actually requires. And it is one of the few that holds up when the noise gets loud.
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